Wavelet Function Denoising Program M-File for Partial Discharge Data
MATLAB M-file implementation of wavelet-based denoising algorithm for processing partial discharge measurement data with noise reduction capabilities.
Explore MATLAB source code curated for "数据" with clean implementations, documentation, and examples.
MATLAB M-file implementation of wavelet-based denoising algorithm for processing partial discharge measurement data with noise reduction capabilities.
The IPIX Radar Clutter Toolbox is designed for reading and processing radar clutter data with enhanced code-driven analysis capabilities.
Implementation of BP Neural Network for traffic volume prediction using MATLAB 7.0 platform, featuring 3 input nodes and 1 output node trained on 15 datasets (1986-2000), including 9 normal training samples, 3 variable data sets, and 3 testing datasets with neural network optimization capabilities
A comprehensive collection of GPS course implementations in MATLAB, including real-world datasets and practical algorithms for hands-on learning and technical skill development.
An image segmentation program developed with fuzzy logic, featuring image classification, data processing, and supporting robust image analysis capabilities through fuzzy rule-based algorithms.
This MATLAB-based program implements IEEE standard test system case studies, providing data for 9-bus, 14-bus, 30-bus, 39-bus, and 57-bus systems. The data is directly imported into the main power flow program for computational analysis using numerical methods and matrix operations.
MATLAB source code implementation for breast tumor diagnosis based on LVQ neural network classification, including complete dataset and implementation details
Implementation of Fisher Linear Classifier using MATLAB for EEG signal classification with BCI competition datasets, featuring feature extraction and signal pattern recognition
PSO-optimized BP algorithm implementation with ready-to-run code that can be executed after adding dataset
A simple yet effective algorithm for accurately detecting extreme points (local minima and maxima) in data sequences, with implementation insights and practical applications across various domains.